Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "64" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 32 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 32 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460015 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.228201 | -0.138744 | -1.240491 | -0.506545 | -0.879705 | -1.053287 | 1.762781 | -0.209339 | 0.5455 | 0.5367 | 0.3374 | nan | nan |
| 2460014 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.203733 | -0.139249 | -1.201540 | -0.660767 | -0.457624 | -0.806111 | 4.745121 | 0.188214 | 0.5132 | 0.5005 | 0.3302 | nan | nan |
| 2460013 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.393743 | -0.374380 | -1.186424 | -0.628613 | -0.855818 | -1.033176 | 2.745347 | 0.231320 | 0.5399 | 0.5371 | 0.3443 | nan | nan |
| 2460012 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.454630 | -0.315685 | -1.319066 | -0.781806 | -0.891584 | -0.972315 | 1.856247 | 0.237040 | 0.5437 | 0.5395 | 0.3394 | nan | nan |
| 2460011 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.019707 | -0.098690 | -1.308561 | -1.178651 | 1.011227 | -0.361483 | 1.223132 | 1.211418 | 0.5363 | 0.5370 | 0.3410 | nan | nan |
| 2460010 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.161009 | 0.222102 | -0.744915 | -0.679634 | -0.731866 | -1.533479 | 0.348217 | 0.074598 | 0.5454 | 0.5503 | 0.3447 | nan | nan |
| 2460009 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.426993 | -0.187916 | -1.049152 | -0.589672 | -0.547253 | -0.994927 | 0.551849 | 0.267783 | 0.5505 | 0.5549 | 0.3500 | nan | nan |
| 2460008 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.021033 | 0.331100 | -0.977220 | -0.768266 | -0.391168 | -0.906547 | -0.016260 | 1.118681 | 0.6081 | 0.6154 | 0.3148 | nan | nan |
| 2460007 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.146909 | 0.127729 | -1.175037 | -0.431134 | -0.879407 | -1.105404 | 3.075179 | 0.363492 | 0.5558 | 0.5586 | 0.3352 | nan | nan |
| 2459999 | not_connected | 0.00% | 99.50% | 99.42% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.1557 | 0.1784 | 0.0930 | nan | nan |
| 2459998 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.137082 | 0.045602 | -0.865972 | -0.534806 | -0.897052 | -0.817817 | 5.505394 | 0.487532 | 0.5626 | 0.5588 | 0.3612 | nan | nan |
| 2459997 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.102581 | -0.120982 | -0.666412 | -0.457180 | -0.928435 | -1.204872 | 6.379935 | 0.031313 | 0.5886 | 0.5847 | 0.3673 | nan | nan |
| 2459996 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.193914 | -0.077187 | -0.935485 | -0.395015 | -0.734612 | -1.220619 | 0.716497 | 0.729338 | 0.5842 | 0.5824 | 0.3831 | nan | nan |
| 2459995 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.186435 | -0.342572 | -0.937923 | -0.791279 | -0.309561 | -0.904570 | 1.343816 | -0.024691 | 0.5858 | 0.5825 | 0.3676 | nan | nan |
| 2459994 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.094144 | -0.154208 | -0.885418 | -0.565475 | -0.772352 | -1.096523 | 1.199423 | -0.659012 | 0.5785 | 0.5748 | 0.3620 | nan | nan |
| 2459993 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.328078 | -0.001825 | -0.547435 | -0.778322 | -0.543276 | -0.864250 | 1.574336 | 0.003403 | 0.5737 | 0.5891 | 0.3725 | nan | nan |
| 2459991 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.208175 | 0.089580 | -0.655724 | -0.713106 | -0.422601 | -0.946819 | 0.884903 | 1.896906 | 0.5789 | 0.5648 | 0.3707 | nan | nan |
| 2459990 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.276532 | 0.166802 | -0.819912 | -0.780773 | -0.644271 | -0.710726 | 1.995555 | 5.492268 | 0.5735 | 0.5657 | 0.3676 | nan | nan |
| 2459989 | not_connected | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.085554 | 0.237588 | -0.527754 | -0.462720 | -0.664296 | -1.038264 | 1.319838 | 4.665341 | 0.5731 | 0.5661 | 0.3674 | nan | nan |
| 2459988 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.151407 | 0.142124 | -0.918501 | -0.949399 | -0.230488 | -0.712782 | 2.443720 | 3.788848 | 0.5611 | 0.5549 | 0.3529 | nan | nan |
| 2459987 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.254204 | 0.004332 | -0.678139 | -0.700589 | -0.970438 | -0.851019 | 0.725391 | 0.335022 | 0.5849 | 0.5771 | 0.3592 | nan | nan |
| 2459986 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.140338 | 0.112824 | -0.767338 | -0.936046 | -0.025915 | -0.608897 | 1.986774 | 2.623773 | 0.5972 | 0.5999 | 0.3189 | nan | nan |
| 2459985 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.109197 | -0.193479 | -0.836488 | -0.743662 | -0.663788 | -1.335460 | 0.254890 | -0.191588 | 0.5793 | 0.5737 | 0.3645 | nan | nan |
| 2459984 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.375081 | -0.220681 | -0.695054 | -0.724944 | -1.178910 | -1.342349 | -0.359591 | 0.214625 | 0.5909 | 0.5886 | 0.3413 | nan | nan |
| 2459983 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.025823 | 0.132620 | -0.791500 | -0.863992 | 0.855019 | -0.007482 | 1.890766 | 3.284179 | 0.6145 | 0.6269 | 0.2917 | nan | nan |
| 2459982 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.286030 | -0.460343 | -1.173930 | -0.408686 | -0.331344 | -0.356603 | 0.327478 | 1.622164 | 0.6425 | 0.6398 | 0.2920 | nan | nan |
| 2459981 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.016514 | 0.311364 | -0.699399 | -1.024495 | -0.608677 | -0.164966 | 2.228528 | 0.201372 | 0.5739 | 0.5643 | 0.3604 | nan | nan |
| 2459980 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.012947 | 0.067908 | -0.838389 | -0.902156 | -0.798044 | -1.227712 | -0.164910 | 0.933253 | 0.6221 | 0.6186 | 0.3033 | nan | nan |
| 2459979 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.080937 | 0.405570 | -0.754490 | -0.917646 | -1.099648 | -1.191760 | 1.280771 | -0.447190 | 0.5691 | 0.5635 | 0.3608 | nan | nan |
| 2459978 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.004953 | 0.300565 | -0.721387 | -1.009784 | -0.559215 | -0.891658 | 3.133456 | 0.044098 | 0.5666 | 0.5585 | 0.3690 | nan | nan |
| 2459977 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.045752 | 0.213784 | -0.759517 | -0.851775 | -0.754208 | -1.385597 | -0.022056 | -0.181957 | 0.5391 | 0.5303 | 0.3259 | nan | nan |
| 2459976 | not_connected | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.102095 | 0.275375 | -0.878119 | -1.012455 | -0.563159 | -1.011153 | 1.941751 | -0.304742 | 0.5811 | 0.5731 | 0.3631 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 1.762781 | -0.138744 | -0.228201 | -0.506545 | -1.240491 | -1.053287 | -0.879705 | -0.209339 | 1.762781 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 4.745121 | -0.203733 | -0.139249 | -1.201540 | -0.660767 | -0.457624 | -0.806111 | 4.745121 | 0.188214 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 2.745347 | -0.393743 | -0.374380 | -1.186424 | -0.628613 | -0.855818 | -1.033176 | 2.745347 | 0.231320 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 1.856247 | -0.454630 | -0.315685 | -1.319066 | -0.781806 | -0.891584 | -0.972315 | 1.856247 | 0.237040 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 1.223132 | 0.019707 | -0.098690 | -1.308561 | -1.178651 | 1.011227 | -0.361483 | 1.223132 | 1.211418 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 0.348217 | 0.161009 | 0.222102 | -0.744915 | -0.679634 | -0.731866 | -1.533479 | 0.348217 | 0.074598 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 0.551849 | -0.426993 | -0.187916 | -1.049152 | -0.589672 | -0.547253 | -0.994927 | 0.551849 | 0.267783 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | nn Temporal Discontinuties | 1.118681 | 0.331100 | 0.021033 | -0.768266 | -0.977220 | -0.906547 | -0.391168 | 1.118681 | -0.016260 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 3.075179 | -0.146909 | 0.127729 | -1.175037 | -0.431134 | -0.879407 | -1.105404 | 3.075179 | 0.363492 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 5.505394 | -0.137082 | 0.045602 | -0.865972 | -0.534806 | -0.897052 | -0.817817 | 5.505394 | 0.487532 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 6.379935 | -0.102581 | -0.120982 | -0.666412 | -0.457180 | -0.928435 | -1.204872 | 6.379935 | 0.031313 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | nn Temporal Discontinuties | 0.729338 | -0.193914 | -0.077187 | -0.935485 | -0.395015 | -0.734612 | -1.220619 | 0.716497 | 0.729338 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 1.343816 | -0.186435 | -0.342572 | -0.937923 | -0.791279 | -0.309561 | -0.904570 | 1.343816 | -0.024691 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 1.199423 | -0.094144 | -0.154208 | -0.885418 | -0.565475 | -0.772352 | -1.096523 | 1.199423 | -0.659012 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 1.574336 | 0.328078 | -0.001825 | -0.547435 | -0.778322 | -0.543276 | -0.864250 | 1.574336 | 0.003403 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | nn Temporal Discontinuties | 1.896906 | 0.208175 | 0.089580 | -0.655724 | -0.713106 | -0.422601 | -0.946819 | 0.884903 | 1.896906 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | nn Temporal Discontinuties | 5.492268 | 0.166802 | 0.276532 | -0.780773 | -0.819912 | -0.710726 | -0.644271 | 5.492268 | 1.995555 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | nn Temporal Discontinuties | 4.665341 | 0.237588 | 0.085554 | -0.462720 | -0.527754 | -1.038264 | -0.664296 | 4.665341 | 1.319838 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | nn Temporal Discontinuties | 3.788848 | 0.142124 | 0.151407 | -0.949399 | -0.918501 | -0.712782 | -0.230488 | 3.788848 | 2.443720 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 0.725391 | -0.254204 | 0.004332 | -0.678139 | -0.700589 | -0.970438 | -0.851019 | 0.725391 | 0.335022 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | nn Temporal Discontinuties | 2.623773 | 0.112824 | 0.140338 | -0.936046 | -0.767338 | -0.608897 | -0.025915 | 2.623773 | 1.986774 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 0.254890 | -0.193479 | -0.109197 | -0.743662 | -0.836488 | -1.335460 | -0.663788 | -0.191588 | 0.254890 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | nn Temporal Discontinuties | 0.214625 | -0.375081 | -0.220681 | -0.695054 | -0.724944 | -1.178910 | -1.342349 | -0.359591 | 0.214625 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | nn Temporal Discontinuties | 3.284179 | -0.025823 | 0.132620 | -0.791500 | -0.863992 | 0.855019 | -0.007482 | 1.890766 | 3.284179 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | nn Temporal Discontinuties | 1.622164 | -1.286030 | -0.460343 | -1.173930 | -0.408686 | -0.331344 | -0.356603 | 0.327478 | 1.622164 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 2.228528 | 0.311364 | -0.016514 | -1.024495 | -0.699399 | -0.164966 | -0.608677 | 0.201372 | 2.228528 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | nn Temporal Discontinuties | 0.933253 | 0.067908 | -0.012947 | -0.902156 | -0.838389 | -1.227712 | -0.798044 | 0.933253 | -0.164910 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 1.280771 | 0.080937 | 0.405570 | -0.754490 | -0.917646 | -1.099648 | -1.191760 | 1.280771 | -0.447190 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 3.133456 | 0.300565 | 0.004953 | -1.009784 | -0.721387 | -0.891658 | -0.559215 | 0.044098 | 3.133456 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | nn Shape | 0.213784 | 0.045752 | 0.213784 | -0.759517 | -0.851775 | -0.754208 | -1.385597 | -0.022056 | -0.181957 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 64 | N06 | not_connected | ee Temporal Discontinuties | 1.941751 | 0.275375 | 0.102095 | -1.012455 | -0.878119 | -1.011153 | -0.563159 | -0.304742 | 1.941751 |